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Calculation of Thermodynamic Properties of Bound Water Molecules

  • Ying Yang
  • Amr H. A. Abdallah
  • Markus A. Lill
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1762)

Abstract

Water molecules in the binding site of a protein significantly influence protein structure and function, for example, by mediating protein–ligand interactions or in form of desolvation as driving force for ligand binding. The knowledge about location and thermodynamic properties of water molecules in the binding site is crucial to the understanding of protein function. This chapter describes the method of calculating the location and thermodynamic properties of bound water molecules from molecular dynamics (MD) simulation trajectories. Thermodynamic profiles of water molecules can be calculated either with or without the presence of a bound ligand based on the scientific problem. The location and thermodynamic profile of hydration sites mediating the protein–ligand interactions is important for understanding protein–ligand binding. The protein desolvation free energy can be estimated for any ligand by summation of the hydration site free energies of the displaced hydration sites. The WATsite program with an easy-to-use graphical user interface (GUI) based on PyMOL was developed for those calculations and is discussed in this chapter. The WATsite program and its PyMOL plugin are available free of charge from http://people.pharmacy.purdue.edu/~mlill/software/watsite/version3.shtml.

Key words

Desolvation Hydration site Molecular dynamics Protein desolvation free energy PyMOL Solvation Water thermodynamics Water models Water molecule WATsite 

Notes

Acknowledgment

We thank Andrew McNutt for testing the program and critical reading. The authors gratefully acknowledge a grant from the NIH (GM092855) for partially supporting this research.

References

  1. 1.
    Cheung MS, Garcia AE, Onuchic JN (2002) Protein folding mediated by solvation: water expulsion and formation of the hydrophobic core occur after the structural collapse. Proc Natl Acad Sci U S A 99(2):685–690CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Gao M, Zhu H, Yao XQ, She ZS (2010) Water dynamics clue to key residues in protein folding. Biochem Biophys Res Commun 392(1):95–99CrossRefPubMedGoogle Scholar
  3. 3.
    Kovacs IA, Szalay MS, Csermely P (2005) Water and molecular chaperones act as weak links of protein folding networks: energy landscape and punctuated equilibrium changes point towards a game theory of proteins. FEBS Lett 579(11):2254–2260CrossRefPubMedGoogle Scholar
  4. 4.
    Sessions RB, Thomas GL, Parker MJ (2004) Water as a conformational editor in protein folding. J Mol Biol 343(4):1125–1133CrossRefPubMedGoogle Scholar
  5. 5.
    Vajda T, Perczel A (2014) Role of water in protein folding, oligomerization, amyloidosis and miniprotein. J Pept Sci 20(10):747–759CrossRefPubMedGoogle Scholar
  6. 6.
    Zuo GH, Hu J, Fang H (2009) Effect of the ordered water on protein folding: an off-lattice go-like model study. Phys Rev E Stat Nonlinear Soft Matter Phys 79(3 Pt 1):031925CrossRefGoogle Scholar
  7. 7.
    Biela A, Betz M, Heine A, Klebe G (2012) Water makes the difference: rearrangement of water solvation layer triggers non-additivity of functional group contributions in protein-ligand binding. ChemMedChem 7(8):1423–1434CrossRefPubMedGoogle Scholar
  8. 8.
    Breiten B, Lockett M, Sherman W et al (2013) Water networks contribute to enthalpy/entropy compensation in protein-ligand binding. J Am Chem Soc 135(41):15579–15584CrossRefPubMedGoogle Scholar
  9. 9.
    Li Z, Lazaridis T (2006) Thermodynamics of buried water clusters at a protein-ligand binding interface. J Phys Chem B 110(3):1464–1475CrossRefPubMedGoogle Scholar
  10. 10.
    Michel J, Tirado-Rives J, Jorgensen WL (2009) Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization. J Am Chem Soc 131(42):15403–15411CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Baron R, Setny P, McCammon JA (2010) Water in cavity-ligand recognition. J Am Chem Soc 132(34):12091–12097CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Bortolato A, Tehan BG, Bodnarchuk MS, Essex JW, Mason JS (2013) Water network perturbation in ligand binding: adenosine A(2A) antagonists as a case study. J Chem Inf Model 53(7):1700–1713CrossRefPubMedGoogle Scholar
  13. 13.
    Hummer G (2010) Molecular binding: under water’s influence. Nat Chem 2(11):906–907CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Ladbury JE (1996) Just add water! The effect of water on the specificity of protein-ligand binding sites and its potential application to drug design. Chem Biol 3(12):973–980CrossRefPubMedGoogle Scholar
  15. 15.
    Eastman P, Pande VS (2015) OpenMM: a hardware independent framework for molecular simulations. Comput Sci Eng 12(4):34–39CrossRefPubMedGoogle Scholar
  16. 16.
    Case DA, Cerutti DS, Cheatham TE et al (2017) AMBER 16. University of California, San FranciscoGoogle Scholar
  17. 17.
    The PyMOL Molecular Graphics System, version 1.8, Schrödinger, LLCGoogle Scholar
  18. 18.
    Hu B, Lill MA (2014) Watsite: hydration site prediction program with Pymol interface. J Comput Chem 35(16):1255–1260CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Word JM, Lovell SC, Richardson JS et al (1999) Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation. J Mol Biol 285(4):1735–1747CrossRefPubMedGoogle Scholar
  20. 20.
    Maier JA, Martinez C, Kasavajhala K et al (2015) ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J Chem Theory Comput 11(8):3696–3713CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Wang J, Wolf RM, Caldwell JW et al (2004) Development and testing of a general amber force field. J Comput Chem 25(9):1157–1174CrossRefPubMedGoogle Scholar
  22. 22.
    Wang J, Wang W, Kollman PA, Case DA (2006) Automatic atom type and bond type perception in molecular mechanical calculations. J Mol Graph Model 25(2):247–260CrossRefPubMedGoogle Scholar
  23. 23.
    Yang Y, Hu B, Lill MA (2014) Analysis of factors influencing hydration site prediction based on molecular dynamics simulations. J Chem Inf Model 54(10):2987–2995CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Sastry GM, Adzhiqirey M, Day T et al (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27(3):221–234CrossRefPubMedGoogle Scholar
  25. 25.
    Hornak V, Abel R, Okur A et al (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 65(3):712–725CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Lindorff-Larsen K, Piana S, Palmo K et al (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78(8):1950–1958PubMedPubMedCentralGoogle Scholar
  27. 27.
    Zielkiewicz J (2005) Structural properties of water: comparison of the SPC, SPCE, TIP4P, and TIP5P models of water. J Chem Phys 123(10):104501CrossRefPubMedGoogle Scholar
  28. 28.
    Horn HW, Swope WC, Pitera JW (2004) Development of an improved four-site water model for biomolecular simulations: TIP4P-Ew. J Chem Phys 120(20):9665–9678CrossRefPubMedGoogle Scholar
  29. 29.
    Izadi S, Anandakrishnan R, Onufriev AV (2014) Building water models: a different approach. J Phys Chem Lett 5(21):3863–3871CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Heyer LJ, Kruglyak S, Yooseph S (1999) Exploring expression data: identification and analysis of coexpressed genes. Genome Res 9(11):1106–1115CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining (KDD-96). AAAI Press, CAGoogle Scholar
  32. 32.
    Bayly CI, Cieplak P, Cornell W, Kollman PA (1993) A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97:10269–10280CrossRefGoogle Scholar
  33. 33.
    Jakalian A, Jack DB, Bayly CI (2002) Fast, efficient generation of high-quality atomic charges. AM1-BCC model: II. Parameterization and validation. J Comput Chem 23(16):1623–1641CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ying Yang
    • 1
  • Amr H. A. Abdallah
    • 1
  • Markus A. Lill
    • 1
  1. 1.Department of Medicinal Chemistry and Molecular Pharmacology, College of PharmacyPurdue UniversityWest LafayetteUSA

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